Brandview World
Mar
Brand Health Tracking with LLM Equity (Part 3)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
What Is An AI Trust Infrastructure?
In the second blog of our three-part series, we discussed the benefits of tracking brand health to form brand strategies that help improve how AI describe and surface your brand. But aside from understanding the dimensions of brand health and the metrics from which brand messaging can be measured, there is another layer that you would need to consider when building your brand strategies. Sure, your brand is now being represented in AI search results and recommendations, but have you set up your brand to not just catch attention but also gain consumer trust?
We’re in the early days of the AI-driven shopping with brands experimenting on how to best connect with customers and compete in this new landscape. While impressive and promising, consumers are approaching this emerging new shopping experience not without caution and circumspection. PwC’s 2025 Future of Consumer Shopping Survey has 64% of its respondents expressed that it would help them trust AI assistants to shop in their behalf if at least one safeguard is in place. These safeguards include but are not limited to approving all purchases before completion, money-back guarantees, turning off access anytime as well as setting strict spending limits.
This echoes back to the early stages of e-commerce with customers exercising prudence when providing credit card information on websites. The implementation of safeguards like SSL encryption and fraud protection subsequently enabled e-commerce to gain consumer confidence and scale for mass adoption.
Once AI-assisted shopping starts to scale, brands that have incorporated an AI trust infrastructure in their strategies would most likely thrive and surface better than those that don’t. But an AI trust infrastructure goes beyond just implementing safeguards for purchases.
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Building An AI Trust Infrastructure
There are at least a couple of things that could go wrong with AI assistants making your purchases. It could overspend or make unexpected or unauthorized decisions. It could buy the wrong selection because it misinterpreted products. That misunderstanding could be a result of outdated or inaccurate product information, or even an instance of AI hallucination when it had to guess because it has inadequate or misaligned data to work with.
While safeguards like spending limits and final customer approval could circumvent the abovementioned situations, what about for errors it commits that a customer is unable to fix because they don’t know what went wrong or how to resolve it? Now these are just a few examples of how consumer trust could be broken, but from these challenges a brand can base on and build their AI trust infrastructure.
Nowadays, product content are mostly structured to capture human attention and rank favorably with search engine optimization (SEO); with the rise of AI agent shopping, content needs to be just as friendly with generative engine optimization (GEO) by including product data optimized in a machine-readable format. In other words, brand content should start speaking to both customer and AI, with consumer terms mapped into specific attributes to help improve precise product matches.
Brands would also need to constantly monitor the accuracy of their product information and how they show up in AI search results to make corrections or adjustments whenever necessary.
Expanding into the concept of purchasing safeguards, perhaps an even greater degree of trust can be earned if consumers understand the scope of delegating to AI assistants through a clear, accessible and easily configurable presentation of the AI-assisted shopping process. In addition to limits and conditions on the purchasing decisions AI is allowed to make, this could include requiring customer approval under certain parameters, mapping and tracing every decision and action the AI makes throughout the shopping process, as well as the abilities to dispute and/or reverse results. Brands can also explore the option to collaborate with popular AI platforms to extend their suite of purchasing controls and safeguards to customers who prefer to shop in those third-party platforms over purchasing directly at their website.
There is also the question about how sensitive customer data is protected. In the coming age of AI-assisted shopping, this won’t be limited to just payment details but also include contextual data such as preferences, constraints, and intent. Understanding how that data is used, remembered, or protected could help customers make that leap into delegating shopping to an AI agent. This includes what data is being shared and who or which other platforms or companies it’s being shared with.
Brands can offer options to minimize the data being retained or limit the amount of time that information is kept, or even present the choice for guest or one-time shopping where no transaction details are ultimately stored. Customer should feel empowered when it comes to their privacy choices by being presented with clear, visible and configurable options.
And despite the gradual transition to an automated shopping experience, brands shouldn’t forget the value of being able to reach a human representative, especially when things escalate. Customers could feel lost, powerless and frustrated in a situation that could’ve been salvaged with intervention by another human.
Image: Cup of Couple
The Future of Brand Health Tracking
The concept of brand health has been around for more than just three decades but how it’s being tracked moving forward is being rewritten. Just as Generative AI has caught the world’s attention and fascination, LLM equity is quickly gaining steamed across various industries in just these last few years. While AI has a democratizing effect of leveling the field for players of all sizes, companies who are able to understand and leverage brand health tracking with LLM equity would likely emerge as leaders in their sectors.
Brands might not have full control over how they’re described or surfaced by AI, but they could strongly influence how they’re represented by developing coherent and consistent brand messaging reinforced by consumer-earned content built on trust and loyalty.
Image: MrWashingt0n
Featured Image: www.kaboompics.com
Top Image: Sagar Soneji
Mar
Brand Health Tracking with LLM Equity (Part 2)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
Is AI Surfacing Your Brand?
In the first blog of our three-part series, we touched on how AI is reshaping the shopping process from the searching for products up to completing the purchase in the customer’s behalf, and what Large Language Models (LLM) equity means for brand health. To illustrate, when a consumer asks an AI agent like ChatGPT for recommendations on clothing brands, does your clothing line show up? And if it does, how what image is being surfaced for your brand?
By tracking brand health, brands are able to learn not only whether their marketing strategies and creative directions are converting into market share, but also determine performance drivers per platform and digital metric, understand which themes or aspects of their brand resonate with consumers, and assess their “piece” of the LLM pie- or how often LLMs surface or recommend their brand.
Image: Julio Lopez
How Is AI Surfacing Your Brand?
There are several dimensions to brand health which includes the strength of your brand to be picked up and recommended by algorithms, the main themes and imagery associated with your brand by consumers and AI, how often consumers and AI share your content or recommend your brand, how likely your consumers would convert into advocates for your brand, and the perceived value of your brand in terms of pricing, quality and worthiness across different media. These can be boiled down into three main dimensions: brand awareness, brand associations, and brand loyalty. From these three main dimensions, a company can form and anchor their LLM equity strategies for visibility, communication, differentiation or positioning, engagement, attracting potential customers, optimizing marketing spend, as well as pivoting or responding to the competition or other emerging challenges.
The effectivity of brand strategies can be measured in three metrics: alignment, engagement, and intent. Alignment refers to how clearly and consistently your brand messaging, themes and values are being represented and communicated to and by your consumers, engagement is concerned with how customers are interacting with your brand in different media and platforms, while intent looks at how your brand moves audiences to search and look up your products and services. All three consider the strength of your brand messaging and values as reflected through consumer-earned content and digital footprints.
With these sets of brand health dimension and key metrics forming the backbone of brand marketing strategies, brands are not only able to catch attention but also earn consumer trust; not just reach audiences but also influence customer decisions; become not only “first to mind” to consumers but also strongly and coherently presented by LLM platforms as we gradually move into an AI-driven shopping landscape.
Image: Atlantic Ambience
Featured Image: TungArt7
Top Image: Anna Shvets
Mar
Brand Health Tracking with LLM Equity (Part 1)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
AI Is Disrupting The Shopper’s Experience
There’s a paradigm shift in the shopping process and AI is the driving force behind this change. Shoppers are no longer just searching online or scrolling through websites; they’ve now taken advantage of AI platforms to discover, compare, and even buy products in their behalf.
According to generative engine optimization (GEO) firm The Rank Collective, their analysis of cross-platform AI visibility data revealed that 64% of consumers are now using AI tools to discover and learn about new products, with frequent online shoppers increasing that share to 66%. ChatGPT serves as a starting point for 34% of these high-intent users.
Another study based on two multi-market surveys of 5,000 consumers aged 18-67 comprised of US, UK, Canadian and Australian residents reported that 41% of consumers trust Gen AI search results more than paid search results. That same study- the 2025 Consumer Adoption of AI Report- also found that only 15% trust AI less than search ads.
Additionally, Adyen’s Retail Report shared that 51% of shoppers are open to AI making purchases in their behalf. It also noted that the number of US shoppers using AI assistants rose from 12% to 35%. With these encouraging figures, 88% of retailers are considering adopting AI to handle the entire shopping process in the shopper’s behalf, with 56% of them prioritizing this technology for 2026.
Image: Google DeepMind
LLM Equity and Brand Building
AI has opened up a new world of fast and frictionless shopping experience. While still in its early stages of adoption, companies have begun exploring this new space to understand what challenges it would need to address in order to compete and thrive.
Perhaps a good starting point is understanding Large Language Models (LLM) equity. LLM equity generally refers to ensuring that AI models are fair, unbiased, and accessible across diverse populations, preventing the reinforcement of existing disparities. It requires addressing algorithmic bias in training data specifically with race, gender, and socioeconomic status, especially in the field of healthcare. It’s also concerned with expanding access and at the same time, performing in non-English languages and low-resource settings.
For brand building, LLM equity is more concerned with whether your brand shows up in Gen AI search results and how it’s being represented. What theme or themes are being represented by your brand? Are those themes coherently represented in your social media? Is your current brand representation connecting and engaging with your audience? Is that connection strong enough to not only move consumers to purchase your product but also engage with your content? Is your brand content strong enough to capture the interest and be remembered by prospective consumers?
In other words, understanding LLM equity in brand building is understanding and tracking your brand health.
Image: TyliJura
Featured Image: Shoper.pl
Top Image: Nataliya Vaitkevich
Feb
Tapping Into The Global Consumer Products Market Growth
jerry9789 0 comments Brand Surveys and Testing, Brandview World
SIS International shared that growth for the global consumer products market is predicted to go over $3.6 trillion by 2035, driven by the following key trends:
- Consistently strong demand for essential consumer goods, such as food, beverages, and household products
- Premiumization and brand differentiation in developed markets
- Expansion into emerging markets to tap into rising disposable incomes and urbanization
- Production innovation through sustainability and packaging development
The demand for packaged goods has always been tied to population growth and urbanization, but there has been a noticeable shift to its nature. For consumer products companies looking for their market share of that projected growth in the coming years, they would need to manage and strategize not only against fluctuating input costs and wavering customer loyalty but also the shift from volume to value.
SIS recommends acquiring knowledge in the following areas to take advantage of these trends:
- “Premiumization” Positioning: Test consumer willingness-to-pay before introducing high-price points tier.
- Maneuvering Into New Markets: Understand cultural nuances in high-growth regions to help ascertain your product’s marketability in new target areas.
- Portfolio Optimization: Refine your product offerings by recognizing which product lines to discontinue, repackage, or prioritize investing in.
- Brand Health Tracking: Monitor your brand value to guide decisions on whether to maintain or pivot strategies.
These are just a few examples of how effective consumer research benefits Consumer Products companies. Those who are consistently leveraging insights like these are positioned to tap into new opportunities as trends shift and new markets emerge.
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Feb
What’s Happening Nowadays With Survey Samples? (Part 2)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World, Burning Questions
Why The MR Industry Should Start Collectively Caring About Data Quality
In his recent LinkedIn post, JD Deitch offered two explanations on why the sample market is what it is right now and has been for the past two decades: clients either don’t know or don’t care how bad the quality of data sample they’ve been receiving. Between not knowing and not caring, the latter is the more egregious of the two.
In Part 1 of this series, we touched on the challenges presently faced by the sample market: participant engagement, polling representivity and fraud as illustrated by the Op4G / Slice MR scandal. Of the three, fraud captures the most attention, the one that makes headlines, the one that stirs up the most discussion and calls for resolution. The threat of fraud is in everyone’s mind, and that’s why there are measures and protections in place and constantly being developed to detect and address it.
Fraud, however, may not be the key issue out of the three. In the Greenbook podcast, Deitch had pointed to participant engagement as a long-standing challenge that the industry has always been aware of and has tried multiple times to solve. Fraud has always been tagged with large sum of dollars lost or deceptively gained; what most don’t see or take into account is how much revenue or opportunity is missed because of bad or low quality data generated by poor engagement. Yes, fraud undermines credibility and trust in the industry but there always has been avenues to regain them; market failures due to poor data quality may not be as visible but the damage they create linger and influence. And that damage through the decades has now translated to the indifference clients feel towards sample data being produced. As Deitch puts it, “Most survey research projects just don’t matter enough for clients to demand better.”
The current product coming out of the sample market has been commoditized enough that they hardly affect business decisions. Clients don’t see enough value or endorse the same level of confidence in the present product to justify spending more to learn or find out what people are thinking. And if an alternative like AI comes along, clients are roused enough to spend and explore what the other options offer.
“Companies will always want to know what people think. That need isn’t going away.” And this is why renewed focus on the participant experience becomes key. Rather than settle for respondents who have time to fill out questions, find and attract people that are the most invested and involved in the subject matter. Incentivize them for their time and underscore why their thoughts and feelings matter. Connect and foster a healthy yet professional relationship with them. Encourage them to find or refer similar personalities. Build and maintain an engaged panel of quality participants.
New and emerging technologies excite clients and investors alike so look into leveraging them into your methods and processes. Learn the best way to implement AI. Don’t simply deploy new tech to cut costs and time; discover where AI would complement human talent the most and where human supervision is most critical. Collaborate with tech people and developers to design and build systems and applications aligning with your goals and values.
The level of care and effort market research agencies put into their research work would always reflect in the end-product. At Cascade Strategies, we believe excellent and high quality data resonates, and we’re confident it will strike chords in clients to make them care enough. And when clients care enough, they’ll be willing to find out and demand more.
To learn more about how we leverage inspired human thinking with modern cutting edge technology to achieve high quality market research for our clients, please contact us here.
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Feb
The Travel and Tourism Industry Takes Flight in 2026
jerry9789 0 comments Brand Surveys and Testing, Brandview World
Global tourism is recovering, according to SIS International, reaching $11.7 trillion in 2025 and projected to climb 3.55% to $16.5 trillion by 2035. International visitor spending surpassed pre-pandemic levels by hitting an unprecedented $2.1 trillion globally while cultural tourism is predicted to grow from $1.2 trillion this year to $2.6 trillion by 2035. The sector makes up 10.3% of the global GDP in 2025 and provides 371 million jobs worldwide- a 14M increase from 2024.
However, the US market is behind pre-pandemic records for international arrivals. For travel and tourism companies looking to thrive and take advantage of all that projected growth, SIS lists five critical insights to consider in their strategies:
- Consumers Pay Premium for Personalization – 61% of consumers are willing to spend more with companies offering options to customize and enhance their travel experiences, with top choices like breakfast, room size, and views.
- Consumers are Seeking Wellness Tourism – 44% of high-income travelers helped drive the global growth of wellness tourism to $1 trillion in 2025 while younger customers are quickly adopting wellness trips.
- Meeting Sustainability Expectations – Travelers are now skipping properties that don’t reflect adherence to sustainability standards.
- Managing AI Implementation – From hyper-personalized itineraries to predictive pricing, AI in tourism is booming with 28.7% annual growth projected to be over $5 billion by 2034.
- Addressing Overtourism Anxiety – High tourist volumes at 14 points year-over-year and worries over insufficient amenities rising by 12 points represent growing concerns with overtourism. Formulating dynamic pricing programs, learning about traveler tolerance levels towards crowding, and identifying potential or alternative destination choices to help manage demand are just some of the approaches companies can take to potentially reduce these anxieties.
The next ten years is an exciting time of growth and innovation for the travel and tourism industry. While all that growth is not without its challenges, reaching success is best navigated not by intuition but by a roadmap drawn by actionable and data-backed insights gained from high level market research.
Recognizing psychographics and behavioral patterns to predict booking behavior, mapping and understanding the entire customer journey, creating clear and measurable connections between program initiatives and revenue outcomes: these are just some of the things forward-looking companies can do to prosper In the travel and tourism industry in 2026.
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Top Image: JESHOOTS-com
Jan
Food and Beverage Sector Expects Steady Growth (But It’s Not What You Think It Is)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
According to SIS International, steady consumer demand would buoy the global food and beverage market growth to $11.4 trillion by 2030, with analysts predicting the US market specifically enjoying between 2% and 4% dollar sales increase in 2026. There is a caveat to this, however; this market growth would be reflected in dollars but not in units sold. Volume growth is projected to be flat to slightly negative as consumers continue to develop selective spending and eating habits, with the market’s revenue generated mostly by price increases between 2% and 4%.
SIS further enumerates five critical trends that could form the backbone of food and beverage brand strategies for the coming years:
- What Value Means For Different Consumers – Value isn’t limited to low price anymore and effective research would help identify which product attributes customers are willing to pay a premium or look elsewhere cheaper.
- Private Label Outpacing National Brands – Understanding where brand loyalty ends and products are viewed as commodities could help national brands compete with growing private labels.
- Consumers Favoring Protein and Gut-Friendly Products – Being a health product won’t sell it alone in an era of increasingly health-conscious shoppers; you’ll also need to recognize which health benefits appeal the most to target consumers and credibly communicate these attributes.
- Food and Beverage Experiences Are Evolving Beyond Flavor – Consumer preferences are growing more meticulous and sophisticated nowadays with texture, aroma, visual appeal, and mouthfeel contributing to the lasting impressions a food and beverage product can create.
- Non-alcoholic Beverages Stirring Up Innovation – Understanding the sober shift with the right set of questions opens up opportunities to design and introduce new beverage offerings without struggling much to find its ideal consumer base.
With effective and high quality market research, food and beverage brands can thrive instead of merely getting by during this projected period of steady industry growth. High level market research would confidently inform and shape business decisions with timely and deep insights on today’s food and beverage consumers, borne out of relevant and flexible research methodologies and backed by real-world validation.
In this period of steady customer demand, exploring beyond these five trends and delving deeper into understanding the driving forces of consumer behavior, attitudes and values through excellent market research could mean revenue gains for food and beverage brands, not in dollar growth through price increases but actual, bonafide volume sales.
All Image Credits: Magda Ehlers
Jan
What’s Happening Nowadays With Survey Samples? (Part 1)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World, Burning Questions
What is The Op4G / Slice MR Scandal?
Op4G (Opinions4Good) and its offshoot Slice were US-based market research companies whose senior leaders were indicted in April 2025 for selling fake market research over the course of a 10-year period, generating $10M in fraudulent revenue. While they marketed their business model of maintaining “a quality, engaged membership panel” of individuals eligible to participate in surveys, they began recruiting in 2014 certain individuals called “ants” to complete surveys to increase revenue despite producing fabricated market data. Companies that purchased survey data from Op4G or Slice between 2014 and 2024 are encouraged to contact the U.S. Attorney’s office.
The scheme opens up questions on how much these fraudulent market data has permeated the industry, especially when Op4G and Slice presented their survey findings as high quality backed by ISO certification. It brings to light the importance of upholding transparency and accountability in the market research industry despite the availability of certain shortcuts to cut cost and time.
Image: jesben
What is Enshittification?
The Op4G / Slice MR scandal is perhaps emblematic of the enshittification of platforms. Popularized by Canadian writer Cory Doctorow in a 2022 blog post, Wikipedia defines enshittification as “a process in which two-sided online products and services decline in quality over time.” JD Deitch, who cited in a Greenbook podcast Doctorow’s article as inspiration for writing his ebook, described enshittification as “what happens in platforms when they start to seek yield and profitability and growth.”
Together with Lenny Murphy on that Greenbook podcast, JD touched on how enshittification compounds the long-standing issues in the sample market when it comes to producing high quality and reliable market data: those of participant engagement and polling representivity. The participant experience has been neglected and treated as an afterthought by the industry for so long that attracting a wide and diverse pool of engaged and relevant respondents has remained a constant challenge. When participants aren’t incentivized enough to engage with the survey experience, the quality of the data and insights produced risk falling short of their true potential. And when you simply aren’t attracting enough respondents or even give a reason to change the minds of those who aren’t really inclined to participate in surveys, you’re missing out on the opportunity of tapping into subsets of the population that could’ve given new and interesting perspectives.
The emergence of AI exacerbates issues and attitudes towards the participant experience. When client companies have not just years but decades worth of survey data and studies, they could simply shift spending away from participant-driven research to developing AI that could produce synthetic data from their stock. And when research market companies don’t own or have access to such kind of survey information, desperate firms might resort to taking shortcuts like programmatic sampling or like in the case of Op4G and Slice, fraudulent means to generate survey data and revenue.
The quality of the synthetic data being produced from all that past data and studies comes to mind, too. Yes, it would depend on the quality of the training data Large Language Machines (LLMs) is fed. Excellent synthetic data would enable scaling and efficiency. However, excellent synthetic data would be tethered to the subject matter it excels on; deviation from the subject matter might produce less than desired outputs and far from potential breakthroughs or new discoveries. And despite AI’s best attempts to optimize based on what it was trained on, there’s also always the risk of it hallucinating. When one cares enough to understand, working or investing with flawed data is simply intolerable.
Image: Tumisu
Featured Image: andibreit
Top Image: Tima Miroshnichenko
Jan
Financial Services Sector Expanding Rapidly But There Will Be Growing Pains
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
SIS International says the outlook for the financial services sector is one of solid and consistent growth, expected to reach $47.55 trillion in 2029, but it is not without anxieties. They shared that around three-quarters of financial services executives on a recent survey expressed concerns over their institutions’ ability to navigate economic instability, adapt to emerging technologies and shifting regulations, as well as sustain existing revenue sources in the coming decade.
SIS points to five key trends where financial institutions could focus their research priorities at as the financial services landscape continues to develop and change:
- AI implementation: Despite high technology adoption rates, a good majority of banking customers struggle to trust AI applications.
- Digital Banks: More and more customers are switching to neobanks. Learning the reasons behind this growing preference would help traditional financial organizations reposition themselves while digital banks would benefit from these insights through sustainable growth and expansion.
- Mobile Banking: Digital channels have established themselves as the primary form of interaction between customers and their banks, and fostering engagement and a more personalized experience through research could lead to improved loyalty.
- Expanding Financial Services Options: Delving into growing technology-oriented fronts open up exploring new and additional avenues to offer financial services digitally.
- Addressing Security Concerns: Adopting security measures against fraud and privacy is just the start but targeted research would help recognize which protections build confidence and trust based on the concerns expressed by different customer segments.
High-quality market research would help financial institutions understand what would cause their customers to hesitate or quickly adapt to new technology or measures, the most effective way to communicate benefits and advantages, targeting the most receptive customer segment, and identifying new opportunities and channels, just to name a few.
Overall, effective market research into these five key insights should enable financial organizations to make confident and strategic decisions aligned with business goals.
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Featured Image: Tima Miroshnichenko
Top Image: Jakub Zerdzicki
Dec
Can AI Replace Human Respondents In Qualitative Research?
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World, Burning Questions
Like most industries these days, market research is no stranger to AI with its broad applications including the employment of synthetic respondents, which are individual profiles constructed by Large Language Models (LLMs) from real or simulated data. They offer fast, cheap, and scalable synthetic data that closely mimics how human participants would respond, a boon for quantitative research. But can synthetic respondents be just as effective in qualitative research? Can AI-powered profiles fully take over the role of human respondents in market research?
Image: Diana
Synthetic Respondents and Qualitative Research
L&E Research recently hosted a webinar sharing their findings and observations testing synthetic respondents across a variety of qualitative research tasks. They shared that AI characteristically produces quick, structured, and consistent surface-level insights. It does well with detecting macro trends in usage or preferences, concept screening if you need to compare multiple ideas at scale, and spot issues with survey testing. It is also capable of gap-filling or simulating missing segments from known data, as well as bulk analysis for summarizing large open-ends quickly.
The key takeaway L&E found is that AI can describe what people do, but it falls short of telling why people do it. AI fundamentally excels in following patterns, but it would struggle with finding out the emotional driver, the motivation behind certain responses. AI can match logic but it won’t be able to fill in tone, nuance nor context like human insight and experience can.
Most AI models are also built on public data and may not have access to knowing how real people would respond to certain questions. When the engineers tried to influence AI agents in the direction of how real participants would respond, it rejected this notion and firmly stood by the perspective formed from the vastness of public data.
Additionally, AI can be absolutely and confidently wrong. Synthetic data can look convincingly human but since AI relies on patterns instead of experience, the air of confidence it puts up doesn’t guarantee accuracy.
Of course, the hosts added a disclaimer that this is where synthetic respondents are at right now, as no one could tell how things could possibly be so much different in the years to come. But the continued utilization of AI in market research- or any other industry, for that matter- is inevitable thanks to the operational and executionary efficiency it grants, and that is enough reason to continue studying and developing synthetic respondents.
Image: Ron Lach
Why The Human Factor Matters
In market research, emotions matter and context counts. AI can prove to be a powerful partner but it is no replacement for lived insight or validation. Human researchers are simply going to remain essential.
AI’s inherent structure and consistency is representative of its pursuit of perfection; however, humans aren’t perfect, nor simple. Humans are emotional and oftentimes, irrational. AI participants would respond based on their perfect approximation of how a human being would, but the synthetic logic behind that would be narrower and more consistent, as it discounts the fact that humans are imperfect.
Humans also bring incredible complexity and a broader range of perception to the table. We can contradict ourselves, and this would be natural. One human participant’s perception and experiences could inform the difference in how they respond from the next, while synthetic data would be uniformly shaped by congruence and invariability, no matter how much effort or work is put into making AI come close to mimicking humanlike responses.
The complexity, variability, and randomness of human nature is desirable in qualitative research. The engineers recognized this and cautioned about overly guiding or influencing randomness in AI that it “will hard-code your picture of randomness to the point where it is no longer random.”
AI can quickly give you bulk analysis but you might not want to rush in bringing it to your stakeholders, as they would question and challenge the quality and reliability of synthetic data. Human insight continues to be vital and irreplaceable when it comes to trust, nuance, and real-world complexity in market research.
Image: Kathrine Birch
The Hybrid Approach
At the end of it all, the hosts made a point that the webinar wasn’t meant to scare people away from synthetic data but rather bring a valid conversation on when it makes sense to take advantage or steer clear of AI-generated personas. In fact, they recommended utilizing a hybrid approach of employing virtual respondents and recruiting human participants, striking a delicate balance between synthesis and empathy.
Synthetic data would be great during the early exploratory stages of market research when you want to get an initial pulse check, something quick and good enough before getting people involved. But once you’re at the point when you need to uncover the emotional driver behind responses and decisions, understand or predict behaviors, or even gain a bit more confidence and trust in your findings, that’s when you bring in your human respondents.
This all aligns not only with a recent growing trend of companies coming around from the AI hype of the last few years but also with our stance on the appropriate use of AI, where we advocate for the responsible and ethical use of artificial intelligence. Instead of handing AI complete reins over all aspects of a business- or in this case, all stages of research work- we at Cascade Strategies encourage the thoughtful and practical application of artificial intelligence in combination with or enhanced by human experience, values and discretion.
To find out how our brand of inspired and enlightened human thinking can help you with your market research needs, please contact us here.
Additional Reading:
Can Synthetic Respondents Take Over Surveys?
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